def complete_benchmarks(ctx, param, incomplete):
"Auto-completion for benchmarks."
skip_import()
# check the current incomplete path. If it does not exists, use its parent
# as a starting point for lookup.
incomplete_path = Path(incomplete)
if not incomplete_path.exists():
incomplete_path = incomplete_path.parent
# List all sub directory
all_dirs = [d for d in incomplete_path.glob('*') if d.is_dir()]
all_benchmarks = [
b for b in all_dirs if (Path(b) / "objective.py").exists()
]
# First try to list benchmarks that match the incomplete pattern.
proposed_benchmarks = propose_from_list(all_benchmarks, incomplete)
if len(proposed_benchmarks) > 0:
return proposed_benchmarks
# Else do completion with sub-directories.
matching_dirs = propose_from_list(all_dirs, incomplete)
if len(matching_dirs) == 1:
# If only one matches, complete the folder name and continue completion
# from here.
return complete_benchmarks(ctx, param, matching_dirs[0])
return matching_dirs
def complete_datasets(ctx, param, incomplete):
"Auto-completion for datasets."
skip_import()
benchmark = find_benchmark_in_args(ctx.args)
if benchmark is None:
return []
datasets = [d.lower() for d in benchmark.get_dataset_names()]
return propose_from_list(datasets, incomplete.lower())
def complete_solvers(ctx, param, incomplete):
"Auto-completion for solvers."
skip_import()
benchmark = find_benchmark_in_args(ctx.args)
if benchmark is None:
return []
solvers = [s.lower() for s in benchmark.get_solver_names()]
return propose_from_list(solvers, incomplete.lower())
def get_output_files(ctx, args, incomplete):
"Auto-completion for datasets."
skip_import()
benchmark = find_benchmark_in_args(args)
if benchmark is None:
return [("", 'Benchmark has not been provided before')]
output_folder = benchmark.get_output_folder()
candidates = list(output_folder.glob('*.csv'))
return propose_from_list(candidates, incomplete)
def get_datasets(ctx, args, incomplete):
"Auto-completion for datasets."
skip_import()
benchmark = find_benchmark_in_args(args)
if benchmark is None:
return [("", 'Benchmark has not been provided before')]
datasets = benchmark.list_benchmark_dataset_names()
datasets = [d.lower() for d in datasets]
return propose_from_list(datasets, incomplete.lower())
def get_solvers(ctx, args, incomplete):
"Auto-completion for solvers."
skip_import()
benchmark = find_benchmark_in_args(args)
if benchmark is None:
return [("", 'Benchmark has not been provided before')]
solvers = benchmark.list_benchmark_solver_names()
solvers = [s.lower() for s in solvers]
return propose_from_list(solvers, incomplete.lower())
def complete_output_files(ctx, param, incomplete):
"Auto-completion for output files."
skip_import()
benchmark = find_benchmark_in_args(ctx.args)
if benchmark is None:
return []
output_folder = benchmark.get_output_folder()
candidates = list(output_folder.glob('*.csv'))
return propose_from_list(candidates, incomplete)
def complete_config_files(ctx, param, incomplete):
"Auto-completion for configuration files."
skip_import()
benchmark = find_benchmark_in_args(ctx.args)
if benchmark is None:
return []
benchmark_folder = benchmark.benchmark_dir
# Only use absolute path to make sure we can use relative_to to
# autocompletion with relative paths
cwd = Path().resolve()
candidates = [
p.resolve().relative_to(cwd) for p in benchmark_folder.glob('*.yml')
]
return propose_from_list(candidates, incomplete)
def complete_output_files(ctx, param, incomplete):
"Auto-completion for output files."
skip_import()
benchmark = find_benchmark_in_args(ctx.args)
if benchmark is None:
return []
output_folder = benchmark.get_output_folder()
# Only use absolute path to make sure we can use relative_to to
# autocompletion with relative paths
cwd = Path().resolve()
candidates = [
p.resolve().relative_to(cwd) for ext in ['csv', 'parquet']
for p in output_folder.glob(f"*.{ext}")
]
return propose_from_list(candidates, incomplete)
def get_benchmark(ctx, args, incomplete):
skip_import()
benchmarks = [os.curdir] + [b.path for b in os.scandir('.') if b.is_dir()]
benchmarks = [b for b in benchmarks if (Path(b) / "objective.py").exists()]
return [b for b in benchmarks if incomplete in b]
def get_datasets(ctx, args, incomplete):
skip_import()
benchmark = Benchmark(args[1])
datasets = benchmark.list_benchmark_dataset_names()
datasets = [d.lower() for d in datasets]
return [d for d in datasets if incomplete.lower() in d]
def get_solvers(ctx, args, incomplete):
skip_import()
benchmark = Benchmark(args[1])
solvers = benchmark.list_benchmark_solver_names()
solvers = [s.lower() for s in solvers]
return [s for s in solvers if incomplete.lower() in s]
